Real-time object detection for unmanned aerial vehicles based on vision transformer and edge computing.

Existing lightweight Convolutional Neural Network (CNN) detectors deployed on Unmanned Aerial Vehicle (UAV) platforms struggle with small object recognition and fail to capture long-range spatial dependencies, while standard Vision Transformer (ViT) architectures suffer from quadratic computational complexity that prohibits real-time inference on embedded hardware. This paper bridges this gap by proposing an integrated framework that adapts ViT for UAV-based real-time object detection through edge computing infrastructure. Our work presents three key contributions: (1) a hierarchical attention mechanism with shifted windows that reduces complexity from O(n²) to O(n), (2) a dynamic token pruning strategy that adaptively discards uninformative background tokens based on attention variance, and (3) a dual-mode edge-UAV collaborative architecture enabling seamless switching between autonomous onboard processing and server-assisted computation. The lightweight ViT variant achieves 68% reduction in floating-point operations (FLOPs) while preserving 94.3% relative accuracy. Through systematic optimization combining mixed-precision quantization, structured pruning, and operator fusion, we obtain 11.2× inference speedup over baseline implementations. Experiments on our collected aerial dataset demonstrate 73.9% mAP@0.5:0.95 at 39.2 frames per second (FPS) on NVIDIA Jetson Xavier NX, surpassing YOLOv5s by 4.7% in accuracy under identical real-time constraints. Notably, small object detection improves by 7.4% Average Precision (AP) compared to CNN baselines. Week-long field trials on DJI Matrice 300 RTK validate sustained performance across varying illumination, platform vibration, and intermittent network connectivity, confirming practical viability for time-critical applications including search and rescue, disaster response, and infrastructure inspection.